{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d90cbc83",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "#from vk_download import main\n",
    "import scipy.sparse as sprs\n",
    "import time\n",
    "from tqdm.auto import tqdm\n",
    "import os\n",
    "import time\n",
    "import pandas as pd\n",
    "import random\n",
    "import datetime\n",
    "from tqdm.auto import tqdm\n",
    "from threading import Thread\n",
    "from multiprocessing import Process\n",
    "import numpy as np\n",
    "from scipy.sparse import csr_matrix, lil_matrix\n",
    "import scipy.sparse as sprs\n",
    "import os\n",
    "from collections import OrderedDict\n",
    "import shutil\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import networkx as nx\n",
    "from IPython.display import clear_output\n",
    "from statsmodels.stats.proportion import proportion_confint\n",
    "from scipy import stats\n",
    "import statsmodels.api as sm\n",
    "from scipy.optimize import minimize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50fd0aff-4718-4d4a-b5e6-194ac40207cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ObjFunction(theta, Distr, N_Succ, N_Obs):\n",
    "    \n",
    "    return - LikelihoodFunction(theta, Distr, N_Succ, N_Obs)  # we will minimize this quantity\n",
    "\n",
    "def LikelihoodFunction(theta, Distr, N_Succ, N_Obs):\n",
    "    \n",
    "    theta = np.array(theta)  # vector of parameters\n",
    "\n",
    "    ProbNullModel = 1 / N_Obs  # the null model\n",
    "    \n",
    "    Vector = np.zeros(N_Succ)  # components of this vector will be tie creation probabilities of successfull observations\n",
    "        \n",
    "    for i in range(len(theta)):\n",
    "        \n",
    "        Vector = Vector + theta[i]*Distr[i]\n",
    "        \n",
    "    Vector = Vector + (1 - theta.sum())*ProbNullModel\n",
    "    \n",
    "    L = np.log(Vector).sum()\n",
    "    \n",
    "    return L "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0be3d60a-4ccb-4f4c-b319-3d88d9eab484",
   "metadata": {},
   "source": [
    "# Choose city"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5c728b84-cb88-47e1-b587-0b475744cf40",
   "metadata": {},
   "outputs": [],
   "source": [
    "#folder = 'MainCity'  # This city was used in analysis\n",
    "\n",
    "# These two cities were used to check robustness of our results\n",
    "\n",
    "#folder = 'City(1)'  # Robustness 1\n",
    "#folder = 'City(2)'  # Robustness 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f595ff1-8743-439d-9f55-93101f4553ec",
   "metadata": {},
   "source": [
    "# Load data and prepare distributions in accordance with the mechanisms presetned in Table 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4a92186a-f9f1-42fa-b62c-078f56c6160d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of observations (unconnected pairs): 289112604\n",
      "\n",
      "Number of successes (unconnected pairs that have become connected): 14404\n"
     ]
    }
   ],
   "source": [
    "Target = np.load(f'{folder}/Masks/Target.npy')\n",
    "\n",
    "MaskBecomeFriends = (Target == 2)\n",
    "MaskWereNotFriends = (Target == 1) | (Target == 2)\n",
    "\n",
    "MaskStaticOpinions = np.load(f'{folder}/Masks/MaskStaticOpinions.npy')\n",
    "\n",
    "FriendsBeforeNumI = np.load(f'{folder}/Vectors/FriendsBeforeNumI.npy')\n",
    "FriendsBeforeNumJ = np.load(f'{folder}/Vectors/FriendsBeforeNumJ.npy')\n",
    "\n",
    "CommFriendsBefore = np.load(f'{folder}/Vectors/CommFriendsBefore.npy')\n",
    "\n",
    "OpinionBeforeI = np.load(f'{folder}/Vectors/OpinionBeforeI.npy')\n",
    "OpinionBeforeJ = np.load(f'{folder}/Vectors/OpinionBeforeJ.npy')\n",
    "\n",
    "ageI = np.load(f'{folder}/Vectors/ageI.npy')\n",
    "ageJ = np.load(f'{folder}/Vectors/ageJ.npy')\n",
    "\n",
    "sexI = np.load(f'{folder}/Vectors/sexI.npy')\n",
    "sexJ = np.load(f'{folder}/Vectors/sexJ.npy')\n",
    "\n",
    "#------------------------------------------------------------------------------------------\n",
    "\n",
    "FriendsBeforeNumI = FriendsBeforeNumI[MaskWereNotFriends*MaskStaticOpinions]\n",
    "FriendsBeforeNumJ = FriendsBeforeNumJ[MaskWereNotFriends*MaskStaticOpinions]\n",
    "\n",
    "MaskBecomeFriends = MaskBecomeFriends[MaskWereNotFriends*MaskStaticOpinions]\n",
    "\n",
    "CommFriendsBefore = CommFriendsBefore[MaskWereNotFriends*MaskStaticOpinions]\n",
    "\n",
    "OpinionBeforeI = OpinionBeforeI[MaskWereNotFriends*MaskStaticOpinions]\n",
    "OpinionBeforeJ = OpinionBeforeJ[MaskWereNotFriends*MaskStaticOpinions]\n",
    "\n",
    "ageI = ageI[MaskWereNotFriends*MaskStaticOpinions]\n",
    "ageJ = ageJ[MaskWereNotFriends*MaskStaticOpinions]\n",
    "\n",
    "sexI = sexI[MaskWereNotFriends*MaskStaticOpinions]\n",
    "sexJ = sexJ[MaskWereNotFriends*MaskStaticOpinions]\n",
    "\n",
    "del(MaskWereNotFriends, MaskStaticOpinions)\n",
    "\n",
    "#------------------------------------------------------------------------------------------\n",
    "\n",
    "FriendsBeforeProduct = FriendsBeforeNumI * FriendsBeforeNumJ\n",
    "\n",
    "del(FriendsBeforeNumI, FriendsBeforeNumJ)\n",
    "\n",
    "PA_Distr = FriendsBeforeProduct / FriendsBeforeProduct.sum()\n",
    "\n",
    "#------------------------------------------------------------------------------------------\n",
    "\n",
    "FriendsBeforeProduct_ = np.copy(FriendsBeforeProduct)\n",
    "\n",
    "FriendsBeforeProductMax = FriendsBeforeProduct_.max()\n",
    "\n",
    "FriendsBeforeProduct_[CommFriendsBefore > 0] =  FriendsBeforeProductMax - FriendsBeforeProduct_[CommFriendsBefore > 0]\n",
    "\n",
    "FriendsBeforeProduct_ = FriendsBeforeProduct_ + 0.1\n",
    "\n",
    "PA_APA_Distr = FriendsBeforeProduct_ / FriendsBeforeProduct_.sum()\n",
    "\n",
    "\n",
    "del(\n",
    "    FriendsBeforeProduct, \n",
    "    FriendsBeforeProduct_,\n",
    "   )\n",
    "\n",
    "#------------------------------------------------------------------------------------------\n",
    "\n",
    "TransitivityDistr = CommFriendsBefore + 0.1\n",
    "\n",
    "TransitivityDistr = TransitivityDistr / TransitivityDistr.sum()\n",
    "\n",
    "#del(CommFriendsBefore)\n",
    "\n",
    "#------------------------------------------------------------------------------------------\n",
    "\n",
    "OpinionDiffBefore = abs(OpinionBeforeI - OpinionBeforeJ)\n",
    "\n",
    "OpinionSelectivityDistr = 1 - OpinionDiffBefore + 0.01\n",
    "\n",
    "del(OpinionBeforeI, OpinionBeforeJ, OpinionDiffBefore)\n",
    "\n",
    "OpinionSelectivityDistr = OpinionSelectivityDistr / OpinionSelectivityDistr.sum()\n",
    "\n",
    "#------------------------------------------------------------------------------------------\n",
    "\n",
    "ageDiff = abs(ageI - ageJ)\n",
    "\n",
    "AgeSelectivityDistr = ageDiff.max() - ageDiff + 1\n",
    "\n",
    "del(ageDiff)\n",
    "\n",
    "AgeSelectivityDistr = AgeSelectivityDistr / AgeSelectivityDistr.sum()\n",
    "\n",
    "#------------------------------------------------------------------------------------------\n",
    "\n",
    "AgeProduct = ageI * ageJ\n",
    "\n",
    "ActivityDistr = AgeProduct.max() - AgeProduct + 1\n",
    "\n",
    "del(ageI, ageJ)\n",
    "\n",
    "ActivityDistr = ActivityDistr / ActivityDistr.sum()\n",
    "\n",
    "#------------------------------------------------------------------------------------------\n",
    "\n",
    "sexDiff = abs(sexI - sexJ)\n",
    "\n",
    "GenderSelectivityDistr = 1 - sexDiff + 0.1\n",
    "\n",
    "del(sexI, sexJ, sexDiff)\n",
    "\n",
    "GenderSelectivityDistr = GenderSelectivityDistr / GenderSelectivityDistr.sum()\n",
    "\n",
    "#------------------------------------------------------------------------------------------\n",
    "\n",
    "PA_DistrSucc = PA_Distr[MaskBecomeFriends]\n",
    "TransitivityDistrSucc = TransitivityDistr[MaskBecomeFriends]\n",
    "OpinionSelectivityDistrSucc = OpinionSelectivityDistr[MaskBecomeFriends]\n",
    "AgeSelectivityDistrSucc = AgeSelectivityDistr[MaskBecomeFriends]\n",
    "GenderSelectivityDistrSucc = GenderSelectivityDistr[MaskBecomeFriends]\n",
    "\n",
    "ActivityDistrSucc = ActivityDistr[MaskBecomeFriends]\n",
    "\n",
    "PA_APA_DistrSucc = PA_APA_Distr[MaskBecomeFriends]\n",
    "\n",
    "#------------------------------------------------------------------------------------------\n",
    "\n",
    "N_DisjointedPairs = len(PA_Distr)\n",
    "N_NewTies = len(PA_DistrSucc)\n",
    "\n",
    "# We use a pseudo-likelihood function so we have no need in full-size distributions\n",
    "\n",
    "del(\n",
    "    PA_Distr, \n",
    "    #TransitivityDistr, \n",
    "    MaskBecomeFriends, \n",
    "    #OpinionSelectivityDistr, \n",
    "    AgeSelectivityDistr, GenderSelectivityDistr, \n",
    "    PA_APA_Distr, \n",
    "    ActivityDistr)\n",
    "\n",
    "print(f'Number of observations (unconnected pairs): {N_DisjointedPairs}')\n",
    "\n",
    "print('')\n",
    "\n",
    "print(f'Number of successes (unconnected pairs that have become connected): {N_NewTies}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61d02cdf-18cd-4733-8ac5-3d22b6abd4e6",
   "metadata": {},
   "source": [
    "# Glossary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6713bad5-74c5-441f-b17f-6d317796257c",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# PA_DistrSucc                   -   preferential attachment distribution\n",
    "\n",
    "# TransitivityDistrSucc          -   transitivity distribution\n",
    "\n",
    "# PA_APA_DistrSucc               -   corrected preferential attachment distribution  \n",
    "\n",
    "# OpinionSelectivityDistrSucc    -   opinion selectivity distribution\n",
    "\n",
    "# AgeSelectivityDistrSucc        -   age selectivity distribution\n",
    "\n",
    "# GenderSelectivityDistrSucc     -   gender selectivity distribution\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "855ad445-2671-4074-b9e8-cffaa2b1bbfa",
   "metadata": {},
   "source": [
    "# Likelihood functions (single variate analysis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cb6234d7-3c76-4383-acd1-8b051bae49d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# PA mechanims\n",
    "\n",
    "resPA = []\n",
    "Distr = [PA_DistrSucc]\n",
    "\n",
    "ThetaGrid = np.arange(0, 1.05, 0.05)\n",
    "\n",
    "for theta in ThetaGrid:\n",
    "    \n",
    "    Likelihood = LikelihoodFunction([theta], Distr, N_NewTies, N_DisjointedPairs)\n",
    "    \n",
    "    resPA.append(Likelihood)\n",
    "    \n",
    "# Triadic closure\n",
    "\n",
    "resTransitivity = []\n",
    "Distr = [TransitivityDistrSucc]\n",
    "\n",
    "ThetaGrid = np.arange(0, 1.05, 0.05)\n",
    "\n",
    "for theta in ThetaGrid:\n",
    "    \n",
    "    Likelihood = LikelihoodFunction([theta], Distr, N_NewTies, N_DisjointedPairs)\n",
    "    \n",
    "    resTransitivity.append(Likelihood)\n",
    "    \n",
    "# Corrected PA mechanism\n",
    "\n",
    "resPA_APA_DistrSucc = []\n",
    "Distr = [PA_APA_DistrSucc]\n",
    "\n",
    "ThetaGrid = np.arange(0, 1.05, 0.05)\n",
    "\n",
    "for theta in ThetaGrid:\n",
    "    \n",
    "    Likelihood = LikelihoodFunction([theta], Distr, N_NewTies, N_DisjointedPairs)\n",
    "    \n",
    "    resPA_APA_DistrSucc.append(Likelihood)\n",
    "    \n",
    "# Opinion selectivity\n",
    "\n",
    "resOpinionSelectivity = []\n",
    "Distr = [OpinionSelectivityDistrSucc]\n",
    "\n",
    "ThetaGrid = np.arange(0, 1.05, 0.05)\n",
    "\n",
    "for theta in ThetaGrid:\n",
    "    \n",
    "    Likelihood = LikelihoodFunction([theta], Distr, N_NewTies, N_DisjointedPairs)\n",
    "    \n",
    "    resOpinionSelectivity.append(Likelihood)\n",
    "    \n",
    "# Age selectivity\n",
    "\n",
    "resAgeSelectivity = []\n",
    "Distr = [AgeSelectivityDistrSucc]\n",
    "\n",
    "ThetaGrid = np.arange(0, 1.05, 0.05)\n",
    "\n",
    "for theta in ThetaGrid:\n",
    "    \n",
    "    Likelihood = LikelihoodFunction([theta], Distr, N_NewTies, N_DisjointedPairs)\n",
    "    \n",
    "    resAgeSelectivity.append(Likelihood)\n",
    "    \n",
    "# Gender selectivity\n",
    "\n",
    "resGenderSelectivity = []\n",
    "Distr = [GenderSelectivityDistrSucc]\n",
    "\n",
    "ThetaGrid = np.arange(0, 1.05, 0.05)\n",
    "\n",
    "for theta in ThetaGrid:\n",
    "    \n",
    "    Likelihood = LikelihoodFunction([theta], Distr, N_NewTies, N_DisjointedPairs)\n",
    "    \n",
    "    resGenderSelectivity.append(Likelihood)\n",
    "\n",
    "# Age-based activity\n",
    "    \n",
    "resAgeActivity = []\n",
    "Distr = [ActivityDistrSucc]\n",
    "\n",
    "ThetaGrid = np.arange(0, 1.05, 0.05)\n",
    "\n",
    "for theta in ThetaGrid:\n",
    "    \n",
    "    Likelihood = LikelihoodFunction([theta], Distr, N_NewTies, N_DisjointedPairs)\n",
    "    \n",
    "    resAgeActivity.append(Likelihood)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d0c1b21-1bc5-434c-867d-237747ce534b",
   "metadata": {},
   "source": [
    "# Plot Figure 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "70cb1ede-3f9f-4efa-a6fd-f3d7a65084c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x1000 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(0, 1.05, 0.05)\n",
    "\n",
    "fig = plt.figure(figsize=(20, 10), constrained_layout = True)\n",
    "\n",
    "nrwos = 2\n",
    "ncols = 4\n",
    "\n",
    "fontsizeValue = 25\n",
    "TitleSizeValue = 20\n",
    "TickSize=15\n",
    "\n",
    "sub = fig.add_subplot(nrwos, ncols, 1)\n",
    "sub.set_title('A. Preferential attachment', fontsize = TitleSizeValue)\n",
    "sub.plot(x, resPA, lw = 3, color='royalblue')\n",
    "xmax = np.argmax(resPA)\n",
    "sub.plot(x[xmax], resPA[xmax], mec='k', mew=3, ms=15, marker = 'o', color='royalblue')\n",
    "sub.text(x[xmax]-0.1, resPA[xmax]-2000, r'$\\theta = 0.767$', fontsize=fontsizeValue-5)\n",
    "#sub.vlines(xmax, 0, resPA[xmax], linestyles='dashed', lw=3, color='k')\n",
    "#sub.set_xlabel(r\"$\\theta$\", fontsize=fontsizeValue)\n",
    "sub.set_ylabel(\"log likelihood\", fontsize=fontsizeValue)\n",
    "\n",
    "sub.xaxis.set_tick_params(labelsize=TickSize)\n",
    "sub.yaxis.set_tick_params(labelsize=TickSize)\n",
    "\n",
    "sub = fig.add_subplot(nrwos, ncols, 2)\n",
    "sub.set_title('B. Transitivity', fontsize = TitleSizeValue)\n",
    "plt.plot(x, resTransitivity, lw = 3, color='royalblue')\n",
    "xmax = np.argmax(resTransitivity)\n",
    "sub.plot(x[xmax], resTransitivity[xmax], mec='k', mew=3, ms=15, marker = 'o', color='royalblue')\n",
    "sub.text(x[xmax]-0.2, resTransitivity[xmax]-3000, r'$\\theta = 1$', fontsize=fontsizeValue-5)\n",
    "#sub.set_xlabel(r\"$\\theta$\", fontsize=fontsizeValue)\n",
    "#sub.set_ylabel(\"log likelihood\", fontsize=fontsizeValue)\n",
    "\n",
    "sub.xaxis.set_tick_params(labelsize=TickSize)\n",
    "sub.yaxis.set_tick_params(labelsize=TickSize)\n",
    "\n",
    "sub = fig.add_subplot(nrwos, ncols, 3)\n",
    "sub.set_title('C. Corrected preferential attachment', fontsize = TitleSizeValue)\n",
    "plt.plot(x, resPA_APA_DistrSucc, lw = 3, color='royalblue')\n",
    "xmax = np.argmax(resPA_APA_DistrSucc)\n",
    "sub.plot(x[xmax], resPA_APA_DistrSucc[xmax], mec='k', mew=3, ms=15, marker = 'o', color='royalblue')\n",
    "sub.text(x[xmax]-0.1, resPA_APA_DistrSucc[xmax]-3000, r'$\\theta = 0.446$', fontsize=fontsizeValue-5)\n",
    "#sub.set_xlabel(r\"$\\theta$\", fontsize=fontsizeValue)\n",
    "#sub.set_ylabel(\"log likelihood\", fontsize=fontsizeValue)\n",
    "\n",
    "sub.xaxis.set_tick_params(labelsize=TickSize)\n",
    "sub.yaxis.set_tick_params(labelsize=TickSize)\n",
    "\n",
    "sub = fig.add_subplot(nrwos, ncols, 5)\n",
    "sub.set_title('E. Opinion selectivity', fontsize = TitleSizeValue)\n",
    "plt.plot(x, resOpinionSelectivity, lw = 3, color='royalblue')\n",
    "xmax = np.argmax(resOpinionSelectivity)\n",
    "sub.plot(x[xmax], resOpinionSelectivity[xmax], mec='k', mew=3, ms=15, marker = 'o', color='royalblue')\n",
    "sub.text(x[xmax]-0.1, resOpinionSelectivity[xmax]-70, r'$\\theta = 0.552$', fontsize=fontsizeValue-5)\n",
    "sub.set_xlabel(r\"$\\theta$\", fontsize=fontsizeValue)\n",
    "sub.set_ylabel(\"log likelihood\", fontsize=fontsizeValue)\n",
    "\n",
    "sub.xaxis.set_tick_params(labelsize=TickSize)\n",
    "sub.yaxis.set_tick_params(labelsize=TickSize)\n",
    "\n",
    "sub = fig.add_subplot(nrwos, ncols, 6)\n",
    "sub.set_title('F. Age selectivity', fontsize = TitleSizeValue)\n",
    "plt.plot(x, resAgeSelectivity, lw = 3, color='royalblue')\n",
    "xmax = np.argmax(resAgeSelectivity)\n",
    "sub.plot(x[xmax], resAgeSelectivity[xmax], mec='k', mew=3, ms=15, marker = 'o', color='royalblue')\n",
    "sub.text(x[xmax]-0.25, resAgeSelectivity[xmax]-20, r'$\\theta = 1$', fontsize=fontsizeValue-5)\n",
    "sub.set_xlabel(r\"$\\theta$\", fontsize=fontsizeValue)\n",
    "#sub.set_ylabel(\"log likelihood\", fontsize=fontsizeValue)\n",
    "sub.xaxis.set_tick_params(labelsize=TickSize)\n",
    "sub.yaxis.set_tick_params(labelsize=TickSize)\n",
    "\n",
    "sub = fig.add_subplot(nrwos, ncols, 7)\n",
    "sub.set_title('G. Gender selectivity', fontsize = TitleSizeValue)\n",
    "plt.plot(x, resGenderSelectivity, lw = 3, color='royalblue')\n",
    "xmax = np.argmax(resGenderSelectivity)\n",
    "sub.plot(x[xmax], resGenderSelectivity[xmax], mec='k', mew=3, ms=15, marker = 'o', color='royalblue')\n",
    "sub.text(x[xmax]-0.1, resGenderSelectivity[xmax]-1000, r'$\\theta = 0.435$', fontsize=fontsizeValue-5)\n",
    "sub.set_xlabel(r\"$\\theta$\", fontsize=fontsizeValue)\n",
    "#sub.set_ylabel(\"log likelihood\", fontsize=fontsizeValue)\n",
    "\n",
    "sub.xaxis.set_tick_params(labelsize=TickSize)\n",
    "sub.yaxis.set_tick_params(labelsize=TickSize)\n",
    "\n",
    "sub = fig.add_subplot(nrwos, ncols, (4, 8))\n",
    "sub.set_title('D. Age activity', fontsize = TitleSizeValue)\n",
    "sub.plot(x, resAgeActivity, lw = 3, color='royalblue')\n",
    "xmax = np.argmax(resAgeActivity)\n",
    "sub.plot(x[xmax], resAgeActivity[xmax], mec='k', mew=3, ms=15, marker = 'o', color='royalblue')\n",
    "sub.text(x[xmax]-0.4, resAgeActivity[xmax]+3, r'$\\theta = 0.394$', fontsize=fontsizeValue-5)\n",
    "sub.set_xlabel(r\"$\\theta$\", fontsize=fontsizeValue)\n",
    "#sub.set_ylabel(\"log likelihood\", fontsize=fontsizeValue)\n",
    "sub.ticklabel_format(useOffset=False)\n",
    "\n",
    "sub.xaxis.set_tick_params(labelsize=TickSize)\n",
    "sub.yaxis.set_tick_params(labelsize=TickSize)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb9e0680-9512-4894-8941-f41fc74a5ea2",
   "metadata": {},
   "source": [
    "# MLE analysis (Table 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8eb2166e-05f7-497f-9378-a098957ae7e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Study network formation mechanisms in isolation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99e00324-d0bb-4120-a969-59b6f6ed6b0f",
   "metadata": {},
   "source": [
    "### Null model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "b8d325a0-504b-4c91-8117-3340444f3a4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-280623.435\n"
     ]
    }
   ],
   "source": [
    "Distr = [PA_DistrSucc]\n",
    "\n",
    "theta = [0]\n",
    "print(np.round(LikelihoodFunction(theta, Distr, N_NewTies, N_DisjointedPairs), 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b4595e5-8808-4f99-aedd-d9ac36bdb9ca",
   "metadata": {},
   "source": [
    "### PA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "aba733af-39b3-47e3-8a90-32181fafea8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Obj function: 272611.7696974388\n",
      "Obj function (rounded): 272612.0\n",
      "\n",
      "Estimated theta: [0.76712425]\n",
      "Estimated theta (rounded): [0.767]\n"
     ]
    }
   ],
   "source": [
    "Distr = [PA_DistrSucc]\n",
    "\n",
    "ThetaGrid = np.arange(0, 1.05, 0.05)\n",
    "\n",
    "ObjFunctionValues = []\n",
    "Coefficients = []\n",
    "\n",
    "for x01 in ThetaGrid:\n",
    "    \n",
    "    x0 = np.array([x01])\n",
    "    \n",
    "    print(x0)\n",
    "    clear_output(wait=True)\n",
    "\n",
    "    Bounds = (\n",
    "              (0, 1),\n",
    "             )\n",
    "\n",
    "    Constraints = (\n",
    "                   {'type': 'ineq', 'fun': lambda x: 1 - x.sum()},\n",
    "                  )\n",
    "\n",
    "    res = minimize(ObjFunction, \n",
    "                   x0, \n",
    "                   args=(Distr, N_NewTies, N_DisjointedPairs, ), \n",
    "                   bounds=Bounds,\n",
    "                   constraints=Constraints, method='trust-constr')\n",
    "    \n",
    "    ObjFunctionValues.append(res.fun)\n",
    "    Coefficients.append(res.x)\n",
    "\n",
    "ObjFunctionValues = np.array(ObjFunctionValues)\n",
    "\n",
    "index = np.argmin(ObjFunctionValues)\n",
    "\n",
    "print(f'Obj function: {ObjFunctionValues[index]}')\n",
    "print(f'Obj function (rounded): {np.round(ObjFunctionValues[index], 0)}')\n",
    "print('')\n",
    "print(f'Estimated theta: {Coefficients[index]}')\n",
    "print(f'Estimated theta (rounded): {np.round(Coefficients[index], 3)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5d4a15d-231d-48a8-84e8-84dd35df1451",
   "metadata": {},
   "source": [
    "### TrCl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "58100d9a-e35b-4cf4-bf67-31538c08a7fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Obj function: 263490.60503045865\n",
      "Obj function (rounded): 263491.0\n",
      "\n",
      "Estimated theta: [0.99999101]\n",
      "Estimated theta (rounded): [1.]\n"
     ]
    }
   ],
   "source": [
    "Distr = [TransitivityDistrSucc]\n",
    "\n",
    "ThetaGrid = np.arange(0, 1.05, 0.05)\n",
    "\n",
    "ObjFunctionValues = []\n",
    "Coefficients = []\n",
    "\n",
    "for x01 in ThetaGrid:\n",
    "    \n",
    "    x0 = np.array([x01])\n",
    "    \n",
    "    print(x0)\n",
    "    clear_output(wait=True)\n",
    "    \n",
    "    Bounds = ((0, 1),)\n",
    "\n",
    "    Constraints = (\n",
    "                   {'type': 'ineq', 'fun': lambda x: 1 - x.sum()},\n",
    "                  )\n",
    "\n",
    "    res = minimize(ObjFunction, \n",
    "                   x0, \n",
    "                   args=(Distr, N_NewTies, N_DisjointedPairs, ), \n",
    "                   bounds=Bounds,\n",
    "                   constraints=Constraints, method='trust-constr')\n",
    "    \n",
    "    ObjFunctionValues.append(res.fun)\n",
    "    Coefficients.append(res.x)\n",
    "\n",
    "ObjFunctionValues = np.array(ObjFunctionValues)\n",
    "\n",
    "index = np.argmin(ObjFunctionValues)\n",
    "\n",
    "print(f'Obj function: {ObjFunctionValues[index]}')\n",
    "print(f'Obj function (rounded): {np.round(ObjFunctionValues[index], 0)}')\n",
    "print('')\n",
    "print(f'Estimated theta: {Coefficients[index]}')\n",
    "print(f'Estimated theta (rounded): {np.round(Coefficients[index], 3)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bdc144e-189c-45ef-a4d8-88fdfe37492d",
   "metadata": {},
   "source": [
    "### Corrected PA (PA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "b3c4ec2b-8d29-468c-96ca-ee354377f367",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Obj function: 263296.99887652264\n",
      "Obj function (rounded): 263297.0\n",
      "\n",
      "Estimated theta: [0.51271144]\n",
      "Estimated theta (rounded): [0.513]\n"
     ]
    }
   ],
   "source": [
    "Distr = [PA_APA_DistrSucc]\n",
    "\n",
    "ThetaGrid = np.arange(0, 1.05, 0.05)\n",
    "\n",
    "ObjFunctionValues = []\n",
    "Coefficients = []\n",
    "\n",
    "for x01 in ThetaGrid:\n",
    "    \n",
    "    x0 = np.array([x01])\n",
    "    \n",
    "    print(x0)\n",
    "    clear_output(wait=True)\n",
    "    \n",
    "    Bounds = ((0, 1),)\n",
    "\n",
    "    Constraints = (\n",
    "                   {'type': 'ineq', 'fun': lambda x: 1 - x.sum()},\n",
    "                  )\n",
    "    try:\n",
    "        res = minimize(ObjFunction, \n",
    "                       x0, \n",
    "                       args=(Distr, N_NewTies, N_DisjointedPairs, ), \n",
    "                       bounds=Bounds,\n",
    "                       constraints=Constraints, method='trust-constr')\n",
    "\n",
    "        ObjFunctionValues.append(res.fun)\n",
    "        Coefficients.append(res.x)\n",
    "        \n",
    "    except Exception as exception:\n",
    "        \n",
    "        pass\n",
    "\n",
    "ObjFunctionValues = np.array(ObjFunctionValues)\n",
    "\n",
    "index = np.argmin(ObjFunctionValues)\n",
    "\n",
    "print(f'Obj function: {ObjFunctionValues[index]}')\n",
    "print(f'Obj function (rounded): {np.round(ObjFunctionValues[index], 0)}')\n",
    "print('')\n",
    "print(f'Estimated theta: {Coefficients[index]}')\n",
    "print(f'Estimated theta (rounded): {np.round(Coefficients[index], 3)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6ab132a-29e5-44e5-b09f-8e0509872d35",
   "metadata": {},
   "source": [
    "### Opinion selectivity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "0abdd69c-0db1-4273-b337-d745133b7c5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Obj function: 280490.12405091326\n",
      "Obj function (rounded): 280490.0\n",
      "\n",
      "Estimated theta: [0.55231854]\n",
      "Estimated theta (rounded): [0.552]\n"
     ]
    }
   ],
   "source": [
    "Distr = [OpinionSelectivityDistrSucc, ]\n",
    "\n",
    "ThetaGrid = np.arange(0, 1.05, 0.05)\n",
    "\n",
    "ObjFunctionValues = []\n",
    "Coefficients = []\n",
    "\n",
    "for x01 in ThetaGrid:\n",
    "    \n",
    "    x0 = np.array([x01])\n",
    "    \n",
    "    print(x0)\n",
    "    clear_output(wait=True)\n",
    "\n",
    "    Bounds = ((0, 1),)\n",
    "\n",
    "    Constraints = (\n",
    "                   {'type': 'ineq', 'fun': lambda x: 1 - x.sum()},\n",
    "                  )\n",
    "\n",
    "    res = minimize(ObjFunction, \n",
    "                   x0, \n",
    "                   args=(Distr, N_NewTies, N_DisjointedPairs, ), \n",
    "                   bounds=Bounds,\n",
    "                   constraints=Constraints, method='trust-constr')\n",
    "    \n",
    "    ObjFunctionValues.append(res.fun)\n",
    "    Coefficients.append(res.x)\n",
    "\n",
    "ObjFunctionValues = np.array(ObjFunctionValues)\n",
    "\n",
    "index = np.argmin(ObjFunctionValues)\n",
    "\n",
    "print(f'Obj function: {ObjFunctionValues[index]}')\n",
    "print(f'Obj function (rounded): {np.round(ObjFunctionValues[index], 0)}')\n",
    "print('')\n",
    "print(f'Estimated theta: {Coefficients[index]}')\n",
    "print(f'Estimated theta (rounded): {np.round(Coefficients[index], 3)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30a7c682-b269-46a5-9a69-bc61c7e5a244",
   "metadata": {},
   "source": [
    "### Age selectivity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "61dc33df-274b-48f1-8ffa-e4057e5aff1d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Obj function: 279065.9771289486\n",
      "Obj function (rounded): 279066.0\n",
      "\n",
      "Estimated theta: [0.99996398]\n",
      "Estimated theta (rounded): [1.]\n"
     ]
    }
   ],
   "source": [
    "Distr = [AgeSelectivityDistrSucc, ]\n",
    "\n",
    "ThetaGrid = np.arange(0, 1.05, 0.05)\n",
    "\n",
    "ObjFunctionValues = []\n",
    "Coefficients = []\n",
    "\n",
    "for x01 in ThetaGrid:\n",
    "    \n",
    "    x0 = np.array([x01])\n",
    "    \n",
    "    print(x0)\n",
    "    clear_output(wait=True)\n",
    "\n",
    "    Bounds = ((0, 1),)\n",
    "\n",
    "    Constraints = (\n",
    "                   {'type': 'ineq', 'fun': lambda x: 1 - x.sum()},\n",
    "                  )\n",
    "\n",
    "    res = minimize(ObjFunction, \n",
    "                   x0, \n",
    "                   args=(Distr, N_NewTies, N_DisjointedPairs, ), \n",
    "                   bounds=Bounds,\n",
    "                   constraints=Constraints, method='trust-constr')\n",
    "    \n",
    "    ObjFunctionValues.append(res.fun)\n",
    "    Coefficients.append(res.x)\n",
    "\n",
    "ObjFunctionValues = np.array(ObjFunctionValues)\n",
    "\n",
    "index = np.argmin(ObjFunctionValues)\n",
    "\n",
    "print(f'Obj function: {ObjFunctionValues[index]}')\n",
    "print(f'Obj function (rounded): {np.round(ObjFunctionValues[index], 0)}')\n",
    "print('')\n",
    "print(f'Estimated theta: {Coefficients[index]}')\n",
    "print(f'Estimated theta (rounded): {np.round(Coefficients[index], 3)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d79e111c-fc29-4c68-8af8-444caadd53bf",
   "metadata": {},
   "source": [
    "### Gender selectivity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "752bdd08-7db3-49dd-9022-fac7b35d1e6a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Obj function: 279655.77938533947\n",
      "Obj function (rounded): 279656.0\n",
      "\n",
      "Estimated theta: [0.43509017]\n",
      "Estimated theta (rounded): [0.435]\n"
     ]
    }
   ],
   "source": [
    "Distr = [GenderSelectivityDistrSucc, ]\n",
    "\n",
    "ThetaGrid = np.arange(0, 1.05, 0.05)\n",
    "\n",
    "ObjFunctionValues = []\n",
    "Coefficients = []\n",
    "\n",
    "for x01 in ThetaGrid:\n",
    "    \n",
    "    x0 = np.array([x01])\n",
    "    \n",
    "    print(x0)\n",
    "    clear_output(wait=True)\n",
    "\n",
    "    Bounds = ((0, 1),)\n",
    "\n",
    "    Constraints = (\n",
    "                   {'type': 'ineq', 'fun': lambda x: 1 - x.sum()},\n",
    "                  )\n",
    "\n",
    "    res = minimize(ObjFunction, \n",
    "                   x0, \n",
    "                   args=(Distr, N_NewTies, N_DisjointedPairs, ), \n",
    "                   bounds=Bounds,\n",
    "                   constraints=Constraints, method='trust-constr')\n",
    "    \n",
    "    ObjFunctionValues.append(res.fun)\n",
    "    Coefficients.append(res.x)\n",
    "\n",
    "ObjFunctionValues = np.array(ObjFunctionValues)\n",
    "\n",
    "index = np.argmin(ObjFunctionValues)\n",
    "\n",
    "print(f'Obj function: {ObjFunctionValues[index]}')\n",
    "print(f'Obj function (rounded): {np.round(ObjFunctionValues[index], 0)}')\n",
    "print('')\n",
    "print(f'Estimated theta: {Coefficients[index]}')\n",
    "print(f'Estimated theta (rounded): {np.round(Coefficients[index], 3)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f88f6fb-6d86-4ecb-8414-e51c02bbd076",
   "metadata": {},
   "source": [
    "# Multivariate analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "240ff0c5-7cff-478f-8197-21954515ef7b",
   "metadata": {},
   "source": [
    "### PA + Triadic closure + Opinion selectivity + Age selectivity + Gender selectivity + Age-based activity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a514edb2-dd44-48ad-9c19-b9645da891d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Obj function: 262779.8453519922\n",
      "Obj function (rounded): 262780.0\n",
      "\n",
      "Estimated theta: [1.88485297e-01 8.11514703e-01 3.12706663e-12 5.42448251e-11\n",
      " 7.84737042e-11 3.69439892e-11]\n",
      "Estimated theta (rounded): [0.188 0.812 0.    0.    0.    0.   ]\n"
     ]
    }
   ],
   "source": [
    "Distr = [PA_DistrSucc, TransitivityDistrSucc, \n",
    "         OpinionSelectivityDistrSucc, AgeSelectivityDistrSucc, GenderSelectivityDistrSucc, \n",
    "         ActivityDistrSucc]\n",
    "\n",
    "ThetaGrid = np.arange(0, 1.2, 0.2)\n",
    "\n",
    "ObjFunctionValues = []\n",
    "Coefficients = []\n",
    "\n",
    "for x01 in ThetaGrid:\n",
    "    for x02 in ThetaGrid:\n",
    "        if x01+x02 > 1:\n",
    "            break\n",
    "        else:\n",
    "            for x03 in ThetaGrid:\n",
    "                if x01+x02+x03 > 1:\n",
    "                    break\n",
    "                else:\n",
    "                    for x04 in ThetaGrid:\n",
    "                        if x01+x02+x03+x04 > 1:\n",
    "                            break\n",
    "                        else:\n",
    "                            for x05 in ThetaGrid:\n",
    "                                if x01+x02+x03+x04+x05 > 1:\n",
    "                                    break\n",
    "                                else:\n",
    "                                    for x06 in ThetaGrid:\n",
    "                                        if x01+x02+x03+x04+x05+x06 > 1:\n",
    "                                            break\n",
    "                                        else:\n",
    "                                            x0 = np.array([x01, x02, x03, x04, x05, x06])\n",
    "                                            \n",
    "                                            print(x0)\n",
    "                                            clear_output(wait=True)\n",
    "                                            \n",
    "                                            Bounds = ((0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1))\n",
    "\n",
    "                                            Constraints = (\n",
    "                                                           {'type': 'ineq', 'fun': lambda x: 1 - x.sum()}, \n",
    "                                                          )\n",
    "                                            #if 1 == 1:\n",
    "                                            try:\n",
    "\n",
    "                                                res = minimize(ObjFunction, \n",
    "                                                               x0, \n",
    "                                                               args=(Distr, N_NewTies, N_DisjointedPairs, ), \n",
    "                                                               bounds=Bounds,\n",
    "                                                               constraints=Constraints, method='trust-constr')\n",
    "\n",
    "                                                ObjFunctionValues.append(res.fun)\n",
    "                                                Coefficients.append(res.x)\n",
    "\n",
    "                                            except Exception as exception:\n",
    "                                            #else:\n",
    "                                                pass\n",
    "\n",
    "ObjFunctionValues = np.array(ObjFunctionValues)\n",
    "\n",
    "index = np.argmin(ObjFunctionValues)\n",
    "\n",
    "print(f'Obj function: {ObjFunctionValues[index]}')\n",
    "print(f'Obj function (rounded): {np.round(ObjFunctionValues[index], 0)}')\n",
    "print('')\n",
    "print(f'Estimated theta: {Coefficients[index]}')\n",
    "print(f'Estimated theta (rounded): {np.round(Coefficients[index], 3)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ff00a8f-e6dd-432b-bc67-479192a6ade2",
   "metadata": {},
   "source": [
    "### PA' + Triadic closure + Opinion selectivity + Age selectivity + Gender selectivity + Age-based activity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "986cfcea-b7f2-4abc-a862-202c43eb9006",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Obj function: 261745.79684803536\n",
      "Obj function (rounded): 261746.0\n",
      "\n",
      "Estimated theta: [3.45449094e-01 4.98188519e-01 1.60243724e-10 6.44268768e-10\n",
      " 1.56362386e-01 1.10461396e-10]\n",
      "Estimated theta (rounded): [0.345 0.498 0.    0.    0.156 0.   ]\n"
     ]
    }
   ],
   "source": [
    "Distr = [PA_APA_DistrSucc, TransitivityDistrSucc, \n",
    "         OpinionSelectivityDistrSucc, AgeSelectivityDistrSucc, GenderSelectivityDistrSucc, \n",
    "         ActivityDistrSucc]\n",
    "\n",
    "ThetaGrid = np.arange(0, 1.2, 0.2)\n",
    "\n",
    "ObjFunctionValues = []\n",
    "Coefficients = []\n",
    "\n",
    "for x01 in ThetaGrid:\n",
    "    for x02 in ThetaGrid:\n",
    "        if x01+x02 > 1:\n",
    "            break\n",
    "        else:\n",
    "            for x03 in ThetaGrid:\n",
    "                if x01+x02+x03 > 1:\n",
    "                    break\n",
    "                else:\n",
    "                    for x04 in ThetaGrid:\n",
    "                        if x01+x02+x03+x04 > 1:\n",
    "                            break\n",
    "                        else:\n",
    "                            for x05 in ThetaGrid:\n",
    "                                if x01+x02+x03+x04+x05 > 1:\n",
    "                                    break\n",
    "                                else:\n",
    "                                    for x06 in ThetaGrid:\n",
    "                                        if x01+x02+x03+x04+x05+x06 > 1:\n",
    "                                            break\n",
    "                                        else:\n",
    "                                            x0 = np.array([x01, x02, x03, x04, x05, x06])\n",
    "                                            \n",
    "                                            print(x0)\n",
    "                                            clear_output(wait=True)\n",
    "                                            \n",
    "                                            Bounds = ((0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1))\n",
    "\n",
    "                                            Constraints = (\n",
    "                                                           {'type': 'ineq', 'fun': lambda x: 1 - x.sum()}, \n",
    "                                                          )\n",
    "                                            #if 1 == 1:\n",
    "                                            try:\n",
    "\n",
    "                                                res = minimize(ObjFunction, \n",
    "                                                               x0, \n",
    "                                                               args=(Distr, N_NewTies, N_DisjointedPairs, ), \n",
    "                                                               bounds=Bounds,\n",
    "                                                               constraints=Constraints, method='trust-constr')\n",
    "\n",
    "                                                ObjFunctionValues.append(res.fun)\n",
    "                                                Coefficients.append(res.x)\n",
    "\n",
    "                                            except Exception as exception:\n",
    "                                            #else:\n",
    "                                                pass\n",
    "\n",
    "ObjFunctionValues = np.array(ObjFunctionValues)\n",
    "\n",
    "index = np.argmin(ObjFunctionValues)\n",
    "\n",
    "print(f'Obj function: {ObjFunctionValues[index]}')\n",
    "print(f'Obj function (rounded): {np.round(ObjFunctionValues[index], 0)}')\n",
    "print('')\n",
    "print(f'Estimated theta: {Coefficients[index]}')\n",
    "print(f'Estimated theta (rounded): {np.round(Coefficients[index], 3)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e9544cd-a364-4e9d-96e9-d37cb248b840",
   "metadata": {},
   "source": [
    "# Multivariate MLE + control for degree factor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7ae8876-cdaf-40d4-844c-6c71337825dc",
   "metadata": {},
   "source": [
    "### Choose the degree control mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc32e77a-504f-4925-a929-06e8b825c1b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Mask_Degree_Control = np.load(f'{folder}/Masks/MaskFewFew.npy')\n",
    "#Mask_Degree_Control = np.load(f'{folder}/Masks/MaskFewAvg.npy')\n",
    "#Mask_Degree_Control = np.load(f'{folder}/Masks/MaskFewMany.npy')\n",
    "#Mask_Degree_Control = np.load(f'{folder}/Masks/MaskAvgAvg.npy')\n",
    "#Mask_Degree_Control = np.load(f'{folder}/Masks/MaskAvgMany.npy')\n",
    "Mask_Degree_Control = np.load(f'{folder}/Masks/MaskManyMany.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "8627ea33-588a-4b31-a9aa-2537573b0361",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of observations (unconnected pairs): 33407703\n",
      "\n",
      "Number of successes (unconnected pairs that have become connected): 5276\n"
     ]
    }
   ],
   "source": [
    "Target = np.load(f'{folder}/Masks/Target.npy')\n",
    "\n",
    "MaskBecomeFriends = (Target == 2)\n",
    "MaskWereNotFriends = (Target == 1) | (Target == 2)\n",
    "\n",
    "MaskStaticOpinions = np.load(f'{folder}/Masks/MaskStaticOpinions.npy')\n",
    "\n",
    "FriendsBeforeNumI = np.load(f'{folder}/Vectors/FriendsBeforeNumI.npy')\n",
    "FriendsBeforeNumJ = np.load(f'{folder}/Vectors/FriendsBeforeNumJ.npy')\n",
    "\n",
    "CommFriendsBefore = np.load(f'{folder}/Vectors/CommFriendsBefore.npy')\n",
    "\n",
    "OpinionBeforeI = np.load(f'{folder}/Vectors/OpinionBeforeI.npy')\n",
    "OpinionBeforeJ = np.load(f'{folder}/Vectors/OpinionBeforeJ.npy')\n",
    "\n",
    "ageI = np.load(f'{folder}/Vectors/ageI.npy')\n",
    "ageJ = np.load(f'{folder}/Vectors/ageJ.npy')\n",
    "\n",
    "sexI = np.load(f'{folder}/Vectors/sexI.npy')\n",
    "sexJ = np.load(f'{folder}/Vectors/sexJ.npy')\n",
    "\n",
    "#------------------------------------------------------------------------------------------\n",
    "\n",
    "FriendsBeforeNumI = FriendsBeforeNumI[MaskWereNotFriends*MaskStaticOpinions*Mask_Degree_Control]\n",
    "FriendsBeforeNumJ = FriendsBeforeNumJ[MaskWereNotFriends*MaskStaticOpinions*Mask_Degree_Control]\n",
    "\n",
    "MaskBecomeFriends = MaskBecomeFriends[MaskWereNotFriends*MaskStaticOpinions*Mask_Degree_Control]\n",
    "\n",
    "CommFriendsBefore = CommFriendsBefore[MaskWereNotFriends*MaskStaticOpinions*Mask_Degree_Control]\n",
    "\n",
    "OpinionBeforeI = OpinionBeforeI[MaskWereNotFriends*MaskStaticOpinions*Mask_Degree_Control]\n",
    "OpinionBeforeJ = OpinionBeforeJ[MaskWereNotFriends*MaskStaticOpinions*Mask_Degree_Control]\n",
    "\n",
    "ageI = ageI[MaskWereNotFriends*MaskStaticOpinions*Mask_Degree_Control]\n",
    "ageJ = ageJ[MaskWereNotFriends*MaskStaticOpinions*Mask_Degree_Control]\n",
    "\n",
    "sexI = sexI[MaskWereNotFriends*MaskStaticOpinions*Mask_Degree_Control]\n",
    "sexJ = sexJ[MaskWereNotFriends*MaskStaticOpinions*Mask_Degree_Control]\n",
    "\n",
    "del(MaskWereNotFriends, MaskStaticOpinions, Mask_Age_Control)\n",
    "\n",
    "#------------------------------------------------------------------------------------------\n",
    "\n",
    "FriendsBeforeProduct = FriendsBeforeNumI * FriendsBeforeNumJ\n",
    "\n",
    "del(FriendsBeforeNumI, FriendsBeforeNumJ)\n",
    "\n",
    "PA_Distr = FriendsBeforeProduct / FriendsBeforeProduct.sum()\n",
    "\n",
    "#------------------------------------------------------------------------------------------\n",
    "\n",
    "FriendsBeforeProduct_ = np.copy(FriendsBeforeProduct)\n",
    "\n",
    "FriendsBeforeProductMax = FriendsBeforeProduct_.max()\n",
    "\n",
    "FriendsBeforeProduct_[CommFriendsBefore > 0] =  FriendsBeforeProductMax - FriendsBeforeProduct_[CommFriendsBefore > 0]\n",
    "\n",
    "FriendsBeforeProduct_ = FriendsBeforeProduct_ + 0.1\n",
    "\n",
    "PA_APA_Distr = FriendsBeforeProduct_ / FriendsBeforeProduct_.sum()\n",
    "\n",
    "\n",
    "del(\n",
    "    FriendsBeforeProduct, \n",
    "    FriendsBeforeProduct_,\n",
    "   )\n",
    "\n",
    "#------------------------------------------------------------------------------------------\n",
    "\n",
    "TransitivityDistr = CommFriendsBefore + 0.1\n",
    "\n",
    "TransitivityDistr = TransitivityDistr / TransitivityDistr.sum()\n",
    "\n",
    "#del(CommFriendsBefore)\n",
    "\n",
    "#------------------------------------------------------------------------------------------\n",
    "\n",
    "OpinionDiffBefore = abs(OpinionBeforeI - OpinionBeforeJ)\n",
    "\n",
    "OpinionSelectivityDistr = 1 - OpinionDiffBefore + 0.01\n",
    "\n",
    "del(OpinionBeforeI, OpinionBeforeJ, OpinionDiffBefore)\n",
    "\n",
    "OpinionSelectivityDistr = OpinionSelectivityDistr / OpinionSelectivityDistr.sum()\n",
    "\n",
    "#------------------------------------------------------------------------------------------\n",
    "\n",
    "ageDiff = abs(ageI - ageJ)\n",
    "\n",
    "AgeSelectivityDistr = ageDiff.max() - ageDiff + 1\n",
    "\n",
    "del(ageDiff)\n",
    "\n",
    "AgeSelectivityDistr = AgeSelectivityDistr / AgeSelectivityDistr.sum()\n",
    "\n",
    "#------------------------------------------------------------------------------------------\n",
    "\n",
    "AgeProduct = ageI * ageJ\n",
    "\n",
    "ActivityDistr = AgeProduct.max() - AgeProduct + 1\n",
    "\n",
    "del(ageI, ageJ)\n",
    "\n",
    "ActivityDistr = ActivityDistr / ActivityDistr.sum()\n",
    "\n",
    "#------------------------------------------------------------------------------------------\n",
    "\n",
    "sexDiff = abs(sexI - sexJ)\n",
    "\n",
    "GenderSelectivityDistr = 1 - sexDiff + 0.1\n",
    "\n",
    "del(sexI, sexJ, sexDiff)\n",
    "\n",
    "GenderSelectivityDistr = GenderSelectivityDistr / GenderSelectivityDistr.sum()\n",
    "\n",
    "#------------------------------------------------------------------------------------------\n",
    "\n",
    "PA_DistrSucc = PA_Distr[MaskBecomeFriends]\n",
    "TransitivityDistrSucc = TransitivityDistr[MaskBecomeFriends]\n",
    "OpinionSelectivityDistrSucc = OpinionSelectivityDistr[MaskBecomeFriends]\n",
    "AgeSelectivityDistrSucc = AgeSelectivityDistr[MaskBecomeFriends]\n",
    "GenderSelectivityDistrSucc = GenderSelectivityDistr[MaskBecomeFriends]\n",
    "\n",
    "ActivityDistrSucc = ActivityDistr[MaskBecomeFriends]\n",
    "\n",
    "PA_APA_DistrSucc = PA_APA_Distr[MaskBecomeFriends]\n",
    "\n",
    "#------------------------------------------------------------------------------------------\n",
    "\n",
    "N_DisjointedPairs = len(PA_Distr)\n",
    "N_NewTies = len(PA_DistrSucc)\n",
    "\n",
    "# We use a pseudo-likelihood function so we have no need in full-size distributions\n",
    "\n",
    "del(\n",
    "    PA_Distr, \n",
    "    #TransitivityDistr, \n",
    "    MaskBecomeFriends, \n",
    "    #OpinionSelectivityDistr, \n",
    "    AgeSelectivityDistr, GenderSelectivityDistr, \n",
    "    PA_APA_Distr, \n",
    "    ActivityDistr)\n",
    "\n",
    "print(f'Number of observations (unconnected pairs): {N_DisjointedPairs}')\n",
    "\n",
    "print('')\n",
    "\n",
    "print(f'Number of successes (unconnected pairs that have become connected): {N_NewTies}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23f2eb4f-19bb-4812-8d0b-8f83c2237a66",
   "metadata": {},
   "source": [
    "### Triadic closure + Opinion selectivity + Age selectivity + Gender selectivity + Age-based activity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "38272628-5afa-43d1-9753-e09ae22c6b3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Obj function: 83766.3686379575\n",
      "Obj function (rounded): 83766.0\n",
      "\n",
      "Estimated theta: [ 9.99999983e-01 -9.65173604e-09 -9.60836795e-09  5.44167860e-08\n",
      " -9.60906982e-09]\n",
      "Estimated theta (rounded): [ 1. -0. -0.  0. -0.]\n"
     ]
    }
   ],
   "source": [
    "Distr = [\n",
    "         TransitivityDistrSucc, \n",
    "         OpinionSelectivityDistrSucc, AgeSelectivityDistrSucc, GenderSelectivityDistrSucc, \n",
    "         ActivityDistrSucc]\n",
    "\n",
    "ThetaGrid = np.arange(0, 1.2, 0.2)\n",
    "\n",
    "ObjFunctionValues = []\n",
    "Coefficients = []\n",
    "\n",
    "for x01 in ThetaGrid:\n",
    "    for x02 in ThetaGrid:\n",
    "        if x01+x02 > 1:\n",
    "            break\n",
    "        else:\n",
    "            for x03 in ThetaGrid:\n",
    "                if x01+x02+x03 > 1:\n",
    "                    break\n",
    "                else:\n",
    "                    for x04 in ThetaGrid:\n",
    "                        if x01+x02+x03+x04 > 1:\n",
    "                            break\n",
    "                        else:\n",
    "                            for x05 in ThetaGrid:\n",
    "                                if x01+x02+x03+x04+x05 > 1:\n",
    "                                    break\n",
    "                                else:\n",
    "                                    x0 = np.array([x01, x02, x03, x04, x05])\n",
    "                                            \n",
    "                                    print(x0)\n",
    "                                    clear_output(wait=True)\n",
    "                                            \n",
    "                                    Bounds = ((0, 1), (0, 1), (0, 1), (0, 1), (0, 1), )\n",
    "\n",
    "                                    Constraints = (\n",
    "                                                           {'type': 'ineq', 'fun': lambda x: 1 - x.sum()}, \n",
    "                                                          )\n",
    "                                            #if 1 == 1:\n",
    "                                    try:\n",
    "\n",
    "                                        res = minimize(ObjFunction, \n",
    "                                                               x0, \n",
    "                                                               args=(Distr, N_NewTies, N_DisjointedPairs, ), \n",
    "                                                               bounds=Bounds,\n",
    "                                                               constraints=Constraints, method='trust-constr')\n",
    "\n",
    "                                        ObjFunctionValues.append(res.fun)\n",
    "                                        Coefficients.append(res.x)\n",
    "\n",
    "                                    except Exception as exception:\n",
    "                                                \n",
    "                                        print(exception)\n",
    "                                            #else:\n",
    "                                        pass\n",
    "\n",
    "ObjFunctionValues = np.array(ObjFunctionValues)\n",
    "\n",
    "index = np.argmin(ObjFunctionValues)\n",
    "\n",
    "print(f'Obj function: {ObjFunctionValues[index]}')\n",
    "print(f'Obj function (rounded): {np.round(ObjFunctionValues[index], 0)}')\n",
    "print('')\n",
    "print(f'Estimated theta: {Coefficients[index]}')\n",
    "print(f'Estimated theta (rounded): {np.round(Coefficients[index], 3)}')"
   ]
  }
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